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A bivariate model was evaluated for the effect of timing of incident syphilis on HIV seroconversion.
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Several different quantitative models were evaluated.
A univariate animal model was used to estimate variance components, while a bivariate model was used to estimate genetic correlations.
15 The bivariate model was fitted with a generalised linear mixed model approach to the bivariate meta-analysis of sensitivity and specificity.
This shows that the bivariate model is the correct specification.
A Latent Class Bivariate Model is proposed.
The genetic bivariate model is illustrated in Figure 1A.
The bivariate and final multivariate model controlling for all items significant in the bivariate model is reported in Table 3.
Variables deemed significant at p < 0.20 in a bivariate model were used in a multivariate model.
Variables that showed significant associations in the bivariate model were introduced in the multivariable models.
Bivariate collinearity among independent variables was evaluated by calculating Spearman's rho correlation coefficient.
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